MDS Incidence - Under-Reported and Under-Diagnosed?

While researching the incidence for myelodysplastic syndrome (MDS), two key issues emerge: under-reporting and under-diagnosis in the MDS population estimates (see Figure 1).

Under-reporting reflects the difference between the diagnosed patient group (“green oval”) and the cases reported in the cancer registry system (“red oval”)

Under-diagnosis reflects the lack of knowledge by primary care physicians who may not refer patients to hematology for a specialty referral and appropriate MDS diagnosis. The diagnosed patient group (“green oval”) may dramatically underestimate the “actual” MDS incidence populations (“blue oval”)

The literature regarding MDS incidence is focused on the “under-diagnosis” issue rather than the “under-reporting” issue.

Figure 1. Under-Reporting and Under Diagnosis Issues in MDS

With regards to the under-reporting issue, this would be a limitation of the cancer registry system in which patients who have been diagnosed are not appropriately categorized as MDS. This would reflect a “lack of completeness” regarding the case ascertainment or capture rates in the registries. In the most recent SEER*Stat 8.1.2 database, the crude rates for MDS (both sexes) for 2001 to 2010 are shown in Figure 2.

As the graphic illustrates, the early years of reportable status (2001 to 2003), the case ascertainment rate may have been incomplete; however, over time, the rates have improved and stabilized, reflecting a better capture rates. One caveat of the cancer registry system is that there are regional variations that may be attributable to differences in case ascertainment. However, the ability to “correct” for these variations is difficult since they may not be consistent across all cancer types.

With regards to the under-diagnosis issue, there have been several recent publications looking at different ways to assess and estimate the “actual” MDS incidence:

Guralnik (Blood, 2004) and Codispoti (Int J Lab Hematol, 2010) have approached the issue looking at reported rates of “unexplained anemia” in the elderly and using diagnostic technology to better estimate the actual “undiagnosed” percentage of MDS in the prevalent elderly population

Goldberg (JCO, 2010) used Medicare claims data to better estimate the “under-diagnosed” MDS population resulting in incidence rates greater than 100 per 100,000 (compared to SEER rates of around 35 per 100,000 for patients over 65 years old). Unfortunately, the claims data used a non-specific ICD-9-CM code (238.7 which includes several other diagnoses besides just MDS) and probably overstates the “undiagnosed” MDS population

Cogle (Blood, 2011) used a similar methodology as Goldberg but relied on the SEER-Medicare database as well as the 5% sample of non-SEER patients from Medicare linked to updated ICD-9-CM codes (based on data from SEER). This study resulted in rates closer to 75 per 100,000 compared to SEER’s 35 per 100,000. However, this study included patients who were also diagnosed with AML that may overestimate the “treatable” incidence for MDS (most patients with AML and MDS will be treated as an AML patient and not as an MDS patient). This assessment may also overestimate the “undiagnosed” population

Overall, the issue of under-diagnosis is important and highly impacts the incidence estimates for MDS. However, the issue of including “undiagnosed” MDS patients in an epidemiology estimate is questionable since these patients may never be diagnosed or are only diagnosed after transformation to AML. Are these “undiagnosed” patients ever going to be considered “treatable” as MDS patients? These are key questions that require addressing before considering including these new estimates into a rigorous epidemiology dataset.